Sparse convolutional neural networks

ABSTRACT

The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/586,668, titled “Sparse Convolutional NeuralNetworks” and filed on Nov. 15, 2017. U.S. Provisional PatentApplication No. 62/586,668 is hereby incorporated by reference herein inits entirety.

FIELD

The present disclosure relates generally to machine learning. Moreparticularly, the present disclosure relates to an autonomous vehiclecomputing system that applies neural networks, such as, for example,convolutional neural networks, to sparse imagery, such as, for example,LIDAR data.

BACKGROUND

An autonomous vehicle is a vehicle that is capable of sensing itsenvironment and navigating with little or no human input. In particular,an autonomous vehicle can observe its surrounding environment using avariety of sensors and can attempt to comprehend the environment byperforming various processing techniques on data collected by thesensors. Given knowledge of its surrounding environment, the autonomousvehicle can identify an appropriate motion path through such surroundingenvironment.

In some instances, an autonomous vehicle can include or otherwise employone or more machine-learned models such as, for example, artificialneural networks to comprehend the surrounding environment and/oridentify an appropriate motion path through such surroundingenvironment.

Artificial neural networks (ANNs or “neural networks”) are an exampleclass of machine-learned models. Neural networks can be trained toperform a task (e.g., make a prediction) by learning from trainingexamples, without task-specific programming. For example, in imagerecognition, neural networks might learn to identify images that containa particular object by analyzing example images that have been manuallylabeled as including the object or labeled as not including the object.

A neural network can include a group of connected nodes, which also canbe referred to as neurons or perceptrons. A neural network can beorganized into one or more layers. Neural networks that include multiplelayers can be referred to as “deep” networks. A deep network can includean input layer, an output layer, and one or more hidden layerspositioned between the input layer and the output layer. The nodes ofthe neural network can be connected or non-fully connected.

One example class of neural networks is convolutional neural networks.In some instances, a convolutional neural network can be deep,feed-forward artificial neural networks that include one or moreconvolutional layers. For example, a convolutional neural network caninclude tens of layers, hundreds of layers, etc. Each convolutionallayer can perform convolutions over input data using learned filters.Filters can also be referred to as kernels. Convolutional neuralnetworks have been successfully applied to analyzing imagery ofdifferent types, including, for example, visual imagery.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a computingsystem. The computing system includes one or more processors. Thecomputing system includes a machine-learned convolutional neuralnetwork. The computing system includes one or more non-transitorycomputer-readable media that store instructions that, when executed bythe one or more processors, cause the computing system to performoperations. The operations include obtaining imagery. The operationsinclude extracting one or more relevant portions of the imagery. The oneor more relevant portions are less than an entirety of the imagery. Theoperations include providing each of the one or more relevant portionsof the imagery to the machine-learned convolutional neural network. Themachine-learned convolutional neural network performs one or moreconvolutions respectively on each of the one or more relevant portions.The operations include receiving a prediction from the machine-learnedconvolutional neural network based at least in part on the one or moreconvolutions respectively performed on each of the one or more relevantportions.

Another example aspect of the present disclosure is directed to anautonomous vehicle that includes the computer system described above.Another example aspect of the present disclosure is directed to acomputer-implemented method that includes performing the operationsdescribed above. Other aspects of the present disclosure are directed tovarious systems, apparatuses, non-transitory computer-readable media,user interfaces, and electronic devices.

Another example aspect of the present disclosure is directed to one ormore non-transitory computer-readable media that store a machine-learnedconvolutional neural network configured to process imagery captured byone or more sensors of an autonomous vehicle. The machine-learnedconvolutional neural network includes one or more sparse convolutionalblocks. Each of the one or more sparse convolutional blocks includes agather layer configured to gather a plurality of non-sparse blocks froma sparse data source and to stack the plurality of non-sparse blocks toform an input tensor. Each of the one or more sparse convolutionalblocks includes one or more convolutional layers configured to performone or more convolutions on the input tensor to generate an outputtensor that contains a plurality of non-sparse output blocks. Each ofthe one or more sparse convolutional blocks includes a scatter layerconfigured to scatter the plurality of non-sparse output blocks of theoutput tensor back to the sparse data source.

Another example aspect of the present disclosure is directed to anautonomous vehicle. The autonomous vehicle includes one or more sensorsthat capture imagery; one or more processors; a machine-learnedconvolutional neural network; and one or more non-transitorycomputer-readable media that store instructions that, when executed bythe one or more processors, cause the autonomous vehicle to performoperations. The operations include obtaining the imagery captured by theone or more sensors of the autonomous vehicle. The operations includeextracting one or more relevant portions of the imagery. The one or morerelevant portions are less than an entirety of the imagery. Theoperations include providing each of the one or more relevant portionsof the imagery to the machine-learned convolutional neural network. Themachine-learned convolutional neural network performs one or moreconvolutions respectively on each of the one or more relevant portions.The operations include receiving a prediction from the machine-learnedconvolutional neural network based at least in part on the one or moreconvolutions respectively performed on each of the one or more relevantportions.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of an example autonomous vehicleaccording to example embodiments of the present disclosure.

FIG. 2 depicts a block diagram of an example computing system accordingto example embodiments of the present disclosure.

FIG. 3A depicts a graphical diagram of example LIDAR imagery accordingto example embodiments of the present disclosure.

FIG. 3B depicts a graphical diagram of an example binary mask accordingto example embodiments of the present disclosure.

FIG. 3C depicts a graphical diagram of example image portions accordingto example embodiments of the present disclosure.

FIG. 4 depicts a flow chart diagram of an example method to apply aconvolutional neural network to sparse imagery according to exampleembodiments of the present disclosure.

FIG. 5 depicts a graphical diagram of an example rectangular tiling forconverting a dense binary mask into sparse locations according toexample embodiments of the present disclosure.

FIG. 6 depicts a graphical diagram of an example sparsegathering/scattering operation as performed by a proposed tiled sparseconvolution module according to example embodiments of the presentdisclosure.

FIG. 7 depicts a graphical diagram of a simplified example of input andoutput tensors according to example embodiments of the presentdisclosure.

FIG. 8 provides graphical diagrams of an example regular residual unitand an example sparse residual unit according to example embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Example aspects of the present disclosure are directed to systems andmethods that apply neural networks such as, for example, convolutionalneural networks, to sparse imagery in an improved manner. For example,the systems and methods of the present disclosure can be included in orotherwise leveraged by an autonomous vehicle. In one example, acomputing system can extract one or more relevant portions from imagery,where the relevant portions are less than an entirety of the imagery. Asone example, the one or more relevant portions of the imagery caninclude one or more non-sparse regions of the imagery. The computingsystem can provide each of the one or more relevant portions of theimagery to a machine-learned convolutional neural network and receive atleast one prediction from the machine-learned convolutional neuralnetwork based at least in part on the one or more relevant portions ofthe imagery. Thus, the computing system can skip performing convolutionsover regions of the imagery where the imagery is sparse and/or regionsof the imagery that are not relevant to the prediction being sought. Byeliminating the performance of convolutions over non-relevant regions ofthe imagery, the systems and methods of the present disclosure cansignificantly reduce the amount of processing required to implement themachine-learned model and, correspondingly, improve the speed at whichpredictions can be obtained.

More particularly, standard deep convolutional neural networks (CNNs)typically apply convolutional operators everywhere on the feature mapacross hundreds of layers, which entails high computation cost for realtime applications. However, as recognized by the present disclosure, formany problems such as object detection and semantic segmentation, a mask(e.g., an attention mask) can be generated or otherwise obtained whichlimits the areas where computation is performed. For example, the maskcan be generated based on the nature of the problem or by segmentationmodels at a lower resolution. As one example, in some implementations,for autonomous driving, object detectors only need to spend computationto detect objects that are on the road or nearby areas. As anotherexample, the mask can be predicted by using a relatively cheap networksuch as saliency prediction and objectness prior or using a part of themain network itself.

According to an aspect of the present disclosure, such masks can be usedto skip computation in the main network. In particular, systems andmethods of the present disclosure leverage the sparsity of the structureof the inputs to perform a novel tiling-based sparse convolutionalalgorithm. Further, the present disclosure proposes Sparse BlocksNetworks (SBNet), which compute convolution on a blockwise decompositionof the mask. These sparse convolution algorithms and networks have beenverified as being effective for performance of camera and/or LIDAR-basedobject detection and semantic segmentation tasks, which have particularapplicability to autonomous vehicle perception and control problems.Furthermore, significant wall-clock speed-ups are possible on standarddetector networks compared to dense convolution, with little to no lossin accuracy (e.g., detection performance).

In some implementations, an autonomous vehicle can be a ground-basedautonomous vehicle (e.g., car, truck, bus, etc.), an air-basedautonomous vehicle (e.g., airplane, drone, helicopter, or otheraircraft), or other types of vehicles (e.g., watercraft). The autonomousvehicle can include a computing system that assists in controlling theautonomous vehicle. In some implementations, the autonomous vehiclecomputing system can include a perception system, a prediction system,and a motion planning system that cooperate to perceive the surroundingenvironment of the autonomous vehicle and determine one or more motionplans for controlling the motion of the autonomous vehicle accordingly.The autonomous vehicle computing system can include one or moreprocessors as well as one or more non-transitory computer-readable mediathat collectively store instructions that, when executed by the one ormore processors, cause the autonomous vehicle computing system toperform various operations as described herein.

In particular, in some implementations, the perception system canreceive sensor data from one or more sensors that are coupled to orotherwise included within the autonomous vehicle. As examples, the oneor more sensors can include a Light Detection and Ranging (LIDAR)system, a Radio Detection and Ranging (RADAR) system, one or morecameras (e.g., visible spectrum cameras, infrared cameras, etc.), and/orother sensors. The sensor data can include information that describesthe location of objects within the surrounding environment of theautonomous vehicle.

In addition to the sensor data, the perception system can retrieve orotherwise obtain map data that provides detailed information about thesurrounding environment of the autonomous vehicle. The map data canprovide information regarding: the identity and location of differentroadways, road segments, buildings, or other items; the location anddirections of traffic lanes (e.g., the location and direction of aparking lane, a turning lane, a bicycle lane, or other lanes within aparticular roadway); traffic control data (e.g., the location andinstructions of signage, traffic lights, or other traffic controldevices); and/or any other map data that provides information thatassists the computing system in comprehending and perceiving itssurrounding environment and its relationship thereto.

The perception system can identify one or more objects that areproximate to the autonomous vehicle based on sensor data received fromthe one or more sensors and/or the map data. In particular, in someimplementations, the perception system can provide, for each object,state data that describes a current state of such object. As examples,the state data for each object can describe an estimate of the object's:current location (also referred to as position); current speed (alsoreferred to as velocity); current acceleration; current heading; currentorientation; size/footprint (e.g., as represented by a boundingpolygon); class (e.g., vehicle vs. pedestrian vs. bicycle), and/or otherstate information.

The prediction system can receive the state data and can predict one ormore future locations for the object(s) identified by the perceptionsystem. For example, various prediction techniques can be used topredict the one or more future locations for the object(s) identified bythe perception system. The prediction system can provide the predictedfuture locations of the objects to the motion planning system.

The motion planning system can determine one or more motion plans forthe autonomous vehicle based at least in part on the state data providedby the perception system and/or the predicted one or more futurelocations for the objects. Stated differently, given information aboutthe current locations of proximate objects and/or predictions about thefuture locations of proximate objects, the motion planning system candetermine motion plan(s) for the autonomous vehicle that best navigatethe vehicle relative to the objects at their current and/or futurelocations.

As an example, in some implementations, the motion planning systemoperates to generate new autonomous motion plan(s) for the autonomousvehicle multiple times per second. Each new autonomous motion plan candescribe motion of the autonomous vehicle over the next several seconds(e.g., 5 seconds). Thus, in some example implementations, the motionplanning system continuously operates to revise or otherwise generate ashort-term motion plan based on the currently available data.

Once the optimization planner has identified the optimal motion plan (orsome other iterative break occurs), the optimal candidate motion plancan be selected and executed by the autonomous vehicle. For example, themotion planning system can provide the selected motion plan to a vehiclecontroller that controls one or more vehicle controls (e.g., actuatorsthat control gas flow, steering, braking, etc.) to execute the selectedmotion plan until the next motion plan is generated.

More generally, many systems, such as, for example, the autonomousvehicle control systems described above, can include, employ, orotherwise leverage one or more convolutional neural networks in order toprocess imagery. Given some input imagery, a convolutional neuralnetwork can make one or more predictions regarding the input imagery. Asexamples, given input imagery that depicts or otherwise describes asurrounding environment of an autonomous vehicle, an autonomous vehiclecontrol system (e.g., perception system, prediction system, motionplanning system, etc.) can employ a convolutional neural network toprovide predictions on the basis of such input imagery. As examples, thepredictions can include predictions that detect objects (e.g.,additional vehicles, pedestrians, bicyclists, etc.) depicted by theimagery; predictions that describe a predicted future trajectory for anobject depicted by the imagery; and/or other tasks, including, forexample, motion planning or map automation.

In some instances, particularly those encountered by autonomous vehiclecontrol systems, the input imagery is sparse in nature. As one example,the input imagery can include LIDAR imagery produced by a LIDAR system.For example, the LIDAR imagery can be a three-dimensional point cloud,where the point cloud is highly sparse. Stated differently, the pointcloud can describe the locations of detected objects inthree-dimensional space and, for many (most) locations inthree-dimensional space, there was not an object detected at suchlocation. Additional examples of input imagery include imagery capturedby one or more cameras or other sensors including, as examples, visiblespectrum imagery (e.g., humanly-perceivable wavelengths); infraredimagery; imagery that depicts RADAR data produced by a RADAR system;heat maps; data visualizations; or other forms of imagery.

Typically, a convolutional neural network contains a number of layers(e.g., tens to hundreds) and each layer is computed sequentially (e.g.,“one by one” after another) in order to provide an output. For example,computing a convolutional layer can include performing a convolution ofa kernel over each and every location in the imagery.

Thus, when applied to sparse inputs such as sparse imagery, theconvolutional neural network will perform convolutions over the sparseregions. However, this represents a significant computational and timeexpense that does not provide correspondingly significant benefits(e.g., does not valuably impact or contribute to the ultimateprediction).

As such, according to an aspect of the present disclosure, a computingsystem can extract one or more relevant portions from imagery, where therelevant portions are less than an entirety of the imagery. As oneexample, the one or more relevant portions of the imagery can includeone or more non-sparse regions of the imagery. The computing system canprovide each of the one or more relevant portions of the imagery to amachine-learned convolutional neural network and receive at least oneprediction from the machine-learned convolutional neural network basedat least in part on the one or more relevant portions of the imagery.

Thus, the computing system can skip performing convolutions over regionsof the imagery where the imagery is sparse or regions of the imagerythat are not relevant to the prediction being sought. By eliminating theperformance of convolutions over non-relevant regions of the imagery,the systems and methods of the present disclosure can significantlyreduce the amount of processing required to implement themachine-learned model and, correspondingly, improve the speed at whichpredictions can be obtained.

In some implementations, to extract the one or more relevant portions ofthe imagery, a computing system can identify one or more non-sparseregions of the imagery. The computing system can extract one or morerelevant portions of the imagery that respectively correspond to the oneor more non-sparse regions. The non-sparse regions can be spatiallynon-sparse or temporally non-sparse.

In some implementations, to extract the one or more relevant portions ofthe imagery, the computing system can generate a binary mask. The binarymask can classify each of a plurality of sections of the imagery aseither sparse or non-sparse. For example, the plurality of sections ofthe imagery can correspond to pixels or voxels of the imagery. Thus, insome examples, the binary mask can indicate, for each pixel or voxelincluded in the imagery, whether such pixel/voxel is sparse ornon-sparse. The computing system can determine the one or more relevantportions of the imagery based at least in part on the binary mask.

As one example, the computing system can generate the binary mask bydividing the imagery into the plurality of sections (e.g., pixels orvoxels). The computing system can determine, for each of the pluralityof sections, an amount of data included in such section. The computingsystem can classify each section as either sparse or non-sparse based atleast in part on the amount of data included in such section. Forexample, in some implementations, if any amount of data is included inthe section (e.g., greater than zero), then the computing system canclassify such section as non-sparse, so that only sections that includeno data at all are classified as sparse. As another example, in someimplementations, the computing system can compare the amount of dataincluded in a section to a threshold amount of data (e.g., five datapoints) to determine whether such section is sparse or non-sparse.

To provide one example, in some implementations, the input imagery canbe a three-dimensional point cloud of LIDAR data. To generate the binarymask, the three-dimensional space can be divided into a plurality ofvoxels. The computing system can determine the amount of data (e.g., thenumber of LIDAR data points) included in each voxel and can classifyeach voxel as either sparse or non-sparse based on the amount of dataincluded in such voxel (e.g., as described above using a threshold ofzero or of some value greater than zero). Thus, in such example, thebinary mask can be a three-dimensional mask that classifies each voxelin three-dimensional space as sparse or non-sparse. This technique canalso be applied to various other forms of three-dimensional imageryother than LIDAR point clouds.

In another example, in some implementations, the input imagery can bethe three-dimensional point cloud of LIDAR data but the imagery can bepreprocessed prior to generation of the binary mask. As one example, thethree-dimensional point can be preprocessed by projecting thethree-dimensional point cloud onto a two-dimensional view (e.g., atop-down or “bird's eye” view). Other preprocessing can optionally beperformed such as, for example, removing outliers, removing points thatcorrespond to the ground prior to projection, removing points associatedwith known objects already included in a map, or other preprocessingtechniques. The binary mask can then be generated with respect to thetwo-dimensional view. For example, the two-dimensional view can bedivided into pixels and each pixel can be classified as sparse ornon-sparse based on the number of data points included in such pixel.

Alternatively or additionally to the mask generation techniquesdescribed above, in some implementations, the computing system caninclude or leverage a machine-learned mask generation model to generatethe binary mask. For example, the computing system can input the imageryinto the machine-learned mask generation model and, in response, receivethe binary mask as an output of the machine-learned mask generationmodel.

As one example, the machine-learned mask generation model can be aneural network, such as, for example, a convolutional neural network.For example, the machine-learned mask generation model can be viewed asan initial portion of a larger convolutional neural network thatprovides the prediction based on the relevant portions of the imagery.In some implementations, the machine-learned mask generation model canbe jointly trained with the convolutional neural network that providesthe prediction based on the relevant portions of the imagery in anend-to-end fashion (e.g., by backpropagating an error through all of thelayers sequentially).

In one example, the machine-learned mask generation model can be trainedor pre-trained based at least in part on training examples that includetraining imagery annotated with ground-truth labels of sparse sectionsand non-sparse sections (e.g., a training image and its corresponding“correct” mask). For example, the preprocessing techniques describedabove (e.g., projection plus amount of data analysis) can be used togenerate training data for the machine-learned mask generation model. Inanother example, the training examples can include segmentation masksused to pre-train the mask generation portion.

In some implementations, at training time, an approximation of a sparseconvolution can be performed by directly multiplying the results with adense binary mask. In some implementations, at training time, the maskis not constrained to be binary in nature, while the mask is constrainedor processed to be binary at inference time. This can avoid the problemof non-differentiable binary variables.

As another example technique to generate the binary mask, in someimplementations, the computing system can generate the binary mask byidentifying a region of interest within the imagery. The computingsystem can classify each section included in the region of interest asnon-sparse while classifying each section that is not included in theregion of interest as sparse.

As one example, the region of interest can be based at least in part oncontext data associated with an autonomous vehicle. For example, thecontext data associated with the autonomous vehicle can include aheading of the autonomous vehicle, a trajectory associated with theautonomous vehicle, and/or other state data associated with theautonomous vehicle or other objects in the surrounding environment.

Thus, to provide one example, portions of the imagery that depictregions of the surrounding environment that are “in front of” theautonomous vehicle and/or regions of the surrounding environment throughwhich the autonomous vehicle expects to travel can be considered to bethe region of interest. As such, portions of the imagery that correspondto regions of the surrounding environment that are behind the autonomousvehicle can be classified as sparse and not-convolved over, therebyimproving processing speed and efficiency. Further, this exampleimplementation can assist in reducing a delay or latency associated withcollection of LIDAR data, since the LIDAR system is not required toperform a complete 360 degree “sweep” but instead the LIDAR data can becollected and processed as soon as the LIDAR system has captured imagerythat corresponds to the region of interest.

As another example, the region of interest can be based at least in parton a confidence metric associated with one or more predictionspreviously obtained relative to a scene depicted by the imagery. Forexample, portions of the imagery for which the corresponding predictionshave low confidence can be included in the region of interest whileportions of the imagery for which the corresponding predictions havehigh confidence can be excluded from the region of interest. In suchfashion, portions of the imagery that have already been analyzed withhigh-confidence can be “ignored” since one would expect any furtherpredictions from the convolutional neural network to be redundant, whileportions of the imagery that have already been analyzed withlow-confidence can be included in the region of interest so that theconvolutional neural network provides an additional prediction withrespect to such portions as “a second opinion”.

To provide an example, an autonomous vehicle can include multiplesensors systems that have different modalities (e.g., cameras versusLIDAR system). First imagery captured by a first sensor (e.g., a camera)can be analyzed to receive a first set of predictions (e.g., predictionsthat detect objects in the surrounding environment as depicted by thecamera imagery). This first set of predictions can have a confidencemetric associated with each prediction. For example, a first detectedobject (e.g., bicyclist) can have a high confidence while a seconddetected object (e.g., pedestrian) or lack of detected object can have alow confidence assigned thereto. As such, a region of interest can bedefined for second imagery captured by a second sensor (e.g., LIDARsystem) based on the confidence metrics applied to the first imagery.For example, portions of the second imagery that may correspond to thefirst detected object may be excluded from the region of interest whileportions of the second imagery that correspond to the second detectedobject (or lack of detected object) may be included in the region ofinterest. As such, portions of the imagery that correspond to previouspredictions of high confidence can be classified as sparse andnot-convolved over, thereby improving processing speed and efficiency.

As another example, the region of interest can be based at least in parton an attention mechanism that tracks, in an iterative fashion, wherewithin the scene the attention of the processing system should befocused. For example, the locations at which portions of imagery weredetermined to be relevant in a past iteration can impact where thesystem searches for relevant imagery in a subsequent iteration (e.g., byguiding the region of interest based on the past imagery and/orpredictions derived from the past imagery).

In some implementations, the region of interest-based masking techniquescan be used in addition to the other masking techniques (e.g.,pixel-by-pixel data analysis) to generate a combined, final mask. Forexample, the final binary mask can be an intersection of the multipleinitial masks.

Once the computing system has generated the binary mask, the computingsystem can determine one or more relevant portions of the imagery basedat least in part on the binary mask.

As one example, in some implementations, the computing system candetermine the one or more relevant portions of the imagery based atleast in part on the binary mask by partitioning the imagery into aplurality of portions and classifying each portion as relevant or notrelevant.

For example, each portion can contain two or more of the plurality ofsections (e.g., pixel/voxel). The portions can be overlapping ornon-overlapping. The portions can be uniformly sized or non-uniformlysized. The portions can have a predefined size or can be dynamicallyfitted around the non-sparse sections. The size of the portions can bedifferent and individually optimized for different applications.

In one example, the portions can be predefined and uniformly sizedrectangles or boxes (e.g., depending on whether the imagery istwo-dimensional or three-dimensional), which can also be referred to as“tiles”. For example, each tile can cover a 9 pixel by 9 pixel area.

The computing system can classify each portion as either relevant or notrelevant based at least in part on the respective classifications of thesections contained in such portion as either sparse or non-sparse. Forexample, in some implementations, if any amount of the sections includedin the portion were classified as non-sparse, then the computing systemcan classify such portion as relevant, so that only portions thatinclude no sections that were classified as non-sparse are classified asnon-relevant. As another example, in some implementations, the computingsystem can compare the number of sections classified as non-sparseincluded in a portion to a threshold number of sections (e.g., threesections) to determine whether such portion is relevant or non-relevant.

In another example, the relevant portions can be fitted around thenon-sparse sections using a clustering/fitting algorithm. For example,the algorithm can seek to minimize both a total number of relevantportions and a total area covered by the relevant portions.

Alternatively or additionally to the techniques for detecting relevantportions described above, in some implementations, the computing systemcan include or leverage a machine-learned portion extraction model toidentify the relevant portions. For example, the computing system caninput the imagery into the machine-learned portion extraction model and,in response, receive identification (e.g., as coordinates of boundingboxes, bounding rectangles, or other bounding shapes) as an output ofthe machine-learned portion extraction model.

As one example, the machine-learned mask portion extraction can be aneural network, such as, for example, a convolutional neural network.For example, the machine-learned portion extraction model can be viewedas an initial portion of a larger convolutional neural network thatprovides the prediction based on the relevant portions of the imagery.In some implementations, the machine-learned portion extraction modelcan be jointly trained with the convolutional neural network thatprovides the prediction based on the relevant portions of the imagery inan end-to-end fashion (e.g., by backpropagating an error through all ofthe layers sequentially). In some examples, the portion extraction modelcan be trained on training examples that include imagery annotated withground-truth labels that describe the location of relevant portionswithin the imagery.

Thus, portions (e.g., tiles) of the input imagery can be designated asrelevant and can be extracted from the imagery for input into theconvolutional neural network. For example, extracting the relevantportions of the imagery can include cropping the imagery or otherwiseisolating the imagery data that corresponds to each relevant portion.

Once the one or more relevant regions have been extracted, the computingsystem can provide each relevant region to a convolutional neuralnetwork. In some implementations, providing each relevant region to theconvolutional neural network can include stacking the one or morerelevant portions in a depth-wise fashion to form a tensor and inputtingthe tensor into the convolutional neural network. For example, theimagery can be re-shaped into a batch dimension. For example, aGPU-accelerated library (e.g., CUDNN) can be used to performconvolutions on the relevant regions.

In some implementations, the computing system can provide each relevantregion to the convolutional neural network by inputting each of the oneor more relevant portions into respective parallel instances of themachine-learned convolutional neural network in parallel.

In some implementations, the machine-learned convolutional neuralnetwork performs only valid convolutions in which a kernel size issmaller than a corresponding portion size. In some implementations, allconvolutions after a first convolution can be valid. Performing validconvolutions can eliminate the need to perform costly read/writeoperations since there are not overlapping portions which need to beupdated. In some implementations, if same convolutions are performed,the input can be padded with surrounding zeros.

The computing system can receive a prediction from the convolutionalneural network. As one example, the prediction can include detection ofimagery data that correspond to an object in a surrounding environmentof the autonomous vehicle. As another example, the prediction caninclude a predicted trajectory for an object in a surroundingenvironment of the autonomous vehicle.

In some implementations, receiving the prediction can include patchingone or more prediction results to the imagery, where the one or moreprediction results respectively correspond to the one or more relevantportions.

In some implementations, the machine-learned convolutional neuralnetwork includes one or more residual blocks. For example, a residualblock can sum its output with the inputs. For example, a residual blockcan include a convolutional layer, a batch normalization layer, and/or arectification layer (e.g., ReLU). According to an aspect of the presentdisclosure, the residual block can be configured to provide a sparseupdate to hidden features. In particular, the residual block canimplement all three of its layers without pasting the results back tothe imagery. As such, the residual block does not need to merge or writeback at each layer, but can write back directly to the original input(e.g., tensor) at the end of the block by adding to the existing input.This has a number of technical benefits, including increased processingspeed due to the sparse updates and elimination of read/writeoperations.

According to another aspect of the present disclosure, in someimplementations, the size of a kernel can be larger than a portion sizeof the one or more relevant portions. For example, in someimplementations, the kernel size can be roughly the same as the size ofthe imagery as a whole. In some of such implementations, sparsity can beenforced on the kernel based on the one or more relevant portions.

In particular, the locations of identified one or more relevant portionscan be used to enforce sparsity on a larger kernel. For example, thecomputing system can define one or more relevant kernel portions withinthe kernel that correspond to the one or more relevant portionsextracted from the imagery. Computations that correspond to portions ofthe kernel that are not included within such relevant kernel portionscan be omitted, thereby saving processing resources and improvingprocessing speed.

To provide a simplified example for the purpose of illustration, aninput image might have an image size of M×N, while a kernel included inthe convolutional neural network has a kernel size of (M−1)×(N−1). Tocontinue the example, one or more relevant portions of the input imagecan be identified, where each relevant portion has a portion size of,for example, (0.1M)×(0.1N). Rather than computing the entirety of thekernel, which would include a significant number of sparse computations,corresponding relevant portions of the kernel can be defined (e.g.,based on matching locations when the kernel is overlaid upon the inputimage) and computations can be performed only for the relevant portionsof the kernel as respectively applied to the relevant portions of theimage. For example, the relevant portions of the kernel can be sized tobe valid convolutions or same convolutions.

The systems and methods of the present disclosure provide a number oftechnical effects and benefits. As one example, the techniques describedherein enable a computing system to skip performing convolutions overregions of imagery where the imagery is sparse or regions of the imagerythat are not relevant to the prediction being sought. By eliminating theperformance of convolutions over non-relevant regions of the imagery,the systems and methods of the present disclosure can significantlyreduce the amount of processing required to implement themachine-learned model and, correspondingly, improve the speed at whichpredictions can be obtained.

Although the present disclosure is discussed with particular referenceto autonomous vehicles, the systems and methods described herein areapplicable to any convolutional neural networks used for any purpose.Further, although the present disclosure is discussed with particularreference to convolutional networks, the systems and methods describedherein can also be used in conjunction with many different forms ofmachine-learned models in addition or alternatively to convolutionalneural networks.

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Devices and Systems

FIG. 1 depicts a block diagram of an example autonomous vehicle 10according to example embodiments of the present disclosure. Theautonomous vehicle 10 is capable of sensing its environment andnavigating without human input. The autonomous vehicle 10 can be aground-based autonomous vehicle (e.g., car, truck, bus, etc.), anair-based autonomous vehicle (e.g., airplane, drone, helicopter, orother aircraft), or other types of vehicles (e.g., watercraft,rail-based vehicles, etc.).

The autonomous vehicle 10 includes one or more sensors 101, an autonomycomputing system 102, and one or more vehicle controls 107. The autonomycomputing system 102 can assist in controlling the autonomous vehicle10. In particular, the autonomy computing system 102 can receive sensordata from the one or more sensors 101, attempt to comprehend thesurrounding environment by performing various processing techniques ondata collected by the sensors 101, and generate an appropriate motionpath through such surrounding environment. The autonomy computing system102 can control the one or more vehicle controls 107 to operate theautonomous vehicle 10 according to the motion path.

The autonomy computing system 102 includes one or more processors 112and a memory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 114 can store data 116and instructions 118 which are executed by the processor 112 to causeautonomy computing system 102 to perform operations.

As illustrated in FIG. 1, the autonomy computing system 102 can includea perception system 103, a prediction system 104, and a motion planningsystem 105 that cooperate to perceive the surrounding environment of theautonomous vehicle 10 and determine a motion plan for controlling themotion of the autonomous vehicle 10 accordingly.

In particular, in some implementations, the perception system 103 canreceive sensor data from the one or more sensors 101 that are coupled toor otherwise included within the autonomous vehicle 10. As examples, theone or more sensors 101 can include a Light Detection and Ranging(LIDAR) system, a Radio Detection and Ranging (RADAR) system, one ormore cameras (e.g., visible spectrum cameras, infrared cameras, etc.),and/or other sensors. The sensor data can include information thatdescribes the location of objects within the surrounding environment ofthe autonomous vehicle 10.

As one example, for a LIDAR system, the sensor data can include thelocation (e.g., in three-dimensional space relative to the LIDAR system)of a number of points that correspond to objects that have reflected aranging laser. For example, a LIDAR system can measure distances bymeasuring the Time of Flight (TOF) that it takes a short laser pulse totravel from the sensor to an object and back, calculating the distancefrom the known speed of light.

As another example, for a RADAR system, the sensor data can include thelocation (e.g., in three-dimensional space relative to the RADAR system)of a number of points that correspond to objects that have reflected aranging radio wave. For example, radio waves (e.g., pulsed orcontinuous) transmitted by the RADAR system can reflect off an objectand return to a receiver of the RADAR system, giving information aboutthe object's location and speed. Thus, a RADAR system can provide usefulinformation about the current speed of an object.

As yet another example, for one or more cameras, various processingtechniques (e.g., range imaging techniques such as, for example,structure from motion, structured light, stereo triangulation, and/orother techniques) can be performed to identify the location (e.g., inthree-dimensional space relative to the one or more cameras) of a numberof points that correspond to objects that are depicted in imagerycaptured by the one or more cameras. Other sensor systems can identifythe location of points that correspond to objects as well.

As another example, the one or more sensors 101 can include apositioning system. The positioning system can determine a currentposition of the vehicle 10. The positioning system can be any device orcircuitry for analyzing the position of the vehicle 10. For example, thepositioning system can determine position by using one or more ofinertial sensors, a satellite positioning system, based on IP address,by using triangulation and/or proximity to network access points orother network components (e.g., cellular towers, WiFi access points,etc.) and/or other suitable techniques. The position of the vehicle 10can be used by various systems of the autonomy computing system 102.

Thus, the one or more sensors 101 can be used to collect sensor datathat includes information that describes the location (e.g., inthree-dimensional space relative to the autonomous vehicle 10) of pointsthat correspond to objects within the surrounding environment of theautonomous vehicle 10.

In addition to the sensor data, the perception system 103 can retrieveor otherwise obtain map data 126 that provides detailed informationabout the surrounding environment of the autonomous vehicle 10. The mapdata 126 can provide information regarding: the identity and location ofdifferent travelways (e.g., roadways), road segments, buildings, orother items or objects (e.g., lampposts, crosswalks, curbing, etc.); thelocation and directions of traffic lanes (e.g., the location anddirection of a parking lane, a turning lane, a bicycle lane, or otherlanes within a particular roadway or other travelway); traffic controldata (e.g., the location and instructions of signage, traffic lights, orother traffic control devices); and/or any other map data that providesinformation that assists the autonomy computing system 102 incomprehending and perceiving its surrounding environment and itsrelationship thereto.

The perception system 103 can identify one or more objects that areproximate to the autonomous vehicle 10 based on sensor data receivedfrom the one or more sensors 101 and/or the map data 126. In particular,in some implementations, the perception system 103 can determine, foreach object, state data that describes a current state of such object.As examples, the state data for each object can describe an estimate ofthe object's: current location (also referred to as position); currentspeed (also referred to as velocity); current acceleration; currentheading; current orientation; size/footprint (e.g., as represented by abounding shape such as a bounding polygon or polyhedron); class (e.g.,vehicle versus pedestrian versus bicycle versus other); yaw rate; and/orother state information.

In some implementations, the perception system 103 can determine statedata for each object over a number of iterations. In particular, theperception system 103 can update the state data for each object at eachiteration. Thus, the perception system 103 can detect and track objects(e.g., vehicles) that are proximate to the autonomous vehicle 10 overtime.

The prediction system 104 can receive the state data from the perceptionsystem 103 and predict one or more future locations for each objectbased on such state data. For example, the prediction system 104 canpredict where each object will be located within the next 5 seconds, 10seconds, 20 seconds, etc. As one example, an object can be predicted toadhere to its current trajectory according to its current speed. Asanother example, other, more sophisticated prediction techniques ormodeling can be used.

The motion planning system 105 can determine a motion plan for theautonomous vehicle 10 based at least in part on the predicted one ormore future locations for the object and/or the state data for theobject provided by the perception system 103. Stated differently, giveninformation about the current locations of objects and/or predictedfuture locations of proximate objects, the motion planning system 105can determine a motion plan for the autonomous vehicle 10 that bestnavigates the autonomous vehicle 10 relative to the objects at suchlocations.

In particular, according to an aspect of the present disclosure, themotion planning system 105 can evaluate one or more cost functions foreach of one or more candidate motion plans for the autonomous vehicle10. For example, the cost function(s) can describe a cost (e.g., overtime) of adhering to a particular candidate motion plan and/or describea reward for adhering to the particular candidate motion plan. Forexample, the reward can be of opposite sign to the cost.

More particularly, to evaluate the one or more cost functions, themotion planning system 105 can determine a plurality of features thatare within a feature space. For example, the status of each feature canbe derived from the state of the vehicle and/or the respective states ofother objects or aspects of the surrounding environment.

The motion planning system 105 can determine the plurality of featuresfor each vehicle state included in the current candidate motion plan.The motion planning system 105 can determine the plurality of featuresfor each vehicle state included in the candidate motion plan.

The motion planning system 105 can evaluate one or more cost functionsbased on the determined features. For example, in some implementations,the one or more cost functions can include a respective linear cost foreach feature at each state.

The motion planning system 105 can iteratively optimize the one or morecost functions to minimize a total cost associated with the candidatemotion plan. For example, the motion planning system 105 can include anoptimization planner that iteratively optimizes the one or more costfunctions.

Following optimization, the motion planning system 105 can provide theoptimal motion plan to a vehicle controller 106 that controls one ormore vehicle controls 107 (e.g., actuators or other devices that controlgas flow, steering, braking, etc.) to execute the optimal motion plan.

Each of the perception system 103, the prediction system 104, the motionplanning system 105, and the vehicle controller 106 can include computerlogic utilized to provide desired functionality. In someimplementations, each of the perception system 103, the predictionsystem 104, the motion planning system 105, and the vehicle controller106 can be implemented in hardware, firmware, and/or softwarecontrolling a general purpose processor. For example, in someimplementations, each of the perception system 103, the predictionsystem 104, the motion planning system 105, and the vehicle controller106 includes program files stored on a storage device, loaded into amemory and executed by one or more processors. In other implementations,each of the perception system 103, the prediction system 104, the motionplanning system 105, and the vehicle controller 106 includes one or moresets of computer-executable instructions that are stored in a tangiblecomputer-readable storage medium such as RAM hard disk or optical ormagnetic media.

In various implementations, one or more of the perception system 103,the prediction system 104, and/or the motion planning system 105 caninclude or otherwise leverage one or more machine-learned models suchas, for example convolutional neural networks.

FIG. 2 depicts a block diagram of an example computing system 100according to example embodiments of the present disclosure. The examplesystem 100 includes a computing system 102 and a machine learningcomputing system 130 that are communicatively coupled over a network180.

In some implementations, the computing system 102 can perform imageextraction and analysis. In some implementations, the computing system102 can be included in an autonomous vehicle. For example, the computingsystem 102 can be on-board the autonomous vehicle. In otherimplementations, the computing system 102 is not located on-board theautonomous vehicle. For example, the computing system 102 can operateoffline to perform image extraction and analysis. The computing system102 can include one or more distinct physical computing devices.

The computing system 102 includes one or more processors 112 and amemory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flashmemory devices, etc., and combinations thereof.

The memory 114 can store information that can be accessed by the one ormore processors 112. For instance, the memory 114 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 116 that can be obtained, received, accessed, written,manipulated, created, and/or stored. The data 116 can include, forinstance, imagery captured by one or more sensors or other forms ofimagery, as described herein. In some implementations, the computingsystem 102 can obtain data from one or more memory device(s) that areremote from the system 102.

The memory 114 can also store computer-readable instructions 118 thatcan be executed by the one or more processors 112. The instructions 118can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 118 can be executed in logically and/or virtually separatethreads on processor(s) 112.

For example, the memory 114 can store instructions 118 that whenexecuted by the one or more processors 112 cause the one or moreprocessors 112 to perform any of the operations and/or functionsdescribed herein, including, for example, image extraction and analysis.

The computing system 102 can also include an imagery extractor 128. Theimagery extractor 128 can extract one or more portions (e.g., relevantportions) from imagery. For example, the imagery extractor 128 canperform some or all of steps 402 through 406 of method 400 of FIG. 4.The imagery extractor 128 can be implemented in hardware, firmware,and/or software controlling one or more processors.

According to an aspect of the present disclosure, the computing system102 can store or include one or more machine-learned models 110. Asexamples, the machine-learned models 110 can be or can otherwise includevarious machine-learned models such as, for example, neural networks(e.g., deep neural networks), support vector machines, decision trees,ensemble models, k-nearest neighbors models, Bayesian networks, or othertypes of models including linear models and/or non-linear models.Example neural networks include feed-forward neural networks, recurrentneural networks (e.g., long short-term memory recurrent neuralnetworks), convolutional neural networks, and/or other forms of neuralnetworks, or combinations thereof.

In some implementations, the computing system 102 can receive the one ormore machine-learned models 110 from the machine learning computingsystem 130 over network 180 and can store the one or moremachine-learned models 110 in the memory 114. The computing system 102can then use or otherwise implement the one or more machine-learnedmodels 110 (e.g., by processor(s) 112). In particular, the computingsystem 102 can implement the machine learned model(s) 110 to performimage analysis. In one example, the imagery can include imagery capturedby one or more sensors of an autonomous vehicle and the machine-learnedmodel (e.g., convolutional neural network) can detect object(s) in asurrounding environment of the autonomous vehicle, as depicted by theimagery. In another example, the imagery can include imagery captured byone or more sensors of an autonomous vehicle and the machine-learnedmodel (e.g., convolutional neural network) can predict a trajectory foran object in a surrounding environment of the autonomous vehicle.

The machine learning computing system 130 includes one or moreprocessors 132 and a memory 134. The one or more processors 132 can beany suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 134 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 134 can store information that can be accessed by the one ormore processors 132. For instance, the memory 134 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 136 that can be obtained, received, accessed, written,manipulated, created, and/or stored. The data 136 can include, forinstance, imagery as described herein. In some implementations, themachine learning computing system 130 can obtain data from one or morememory device(s) that are remote from the system 130.

The memory 134 can also store computer-readable instructions 138 thatcan be executed by the one or more processors 132. The instructions 138can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 138 can be executed in logically and/or virtually separatethreads on processor(s) 132.

For example, the memory 134 can store instructions 138 that whenexecuted by the one or more processors 132 cause the one or moreprocessors 132 to perform any of the operations and/or functionsdescribed herein, including, for example, image extraction and analysis.

In some implementations, the machine learning computing system 130includes one or more server computing devices. If the machine learningcomputing system 130 includes multiple server computing devices, suchserver computing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition or alternatively to the model(s) 110 at the computing system102, the machine learning computing system 130 can include one or moremachine-learned models 140. As examples, the machine-learned models 140can be or can otherwise include various machine-learned models such as,for example, neural networks (e.g., deep neural networks), supportvector machines, decision trees, ensemble models, k-nearest neighborsmodels, Bayesian networks, or other types of models including linearmodels and/or non-linear models. Example neural networks includefeed-forward neural networks, recurrent neural networks (e.g., longshort-term memory recurrent neural networks), convolutional neuralnetworks, or other forms of neural networks.

As an example, the machine learning computing system 130 can communicatewith the computing system 102 according to a client-server relationship.For example, the machine learning computing system 140 can implement themachine-learned models 140 to provide a web service to the computingsystem 102. For example, the web service can provide image extractionand analysis.

Thus, machine-learned models 110 can located and used at the computingsystem 102 and/or machine-learned models 140 can be located and used atthe machine learning computing system 130.

In some implementations, the machine learning computing system 130and/or the computing system 102 can train the machine-learned models 110and/or 140 through use of a model trainer 160. The model trainer 160 cantrain the machine-learned models 110 and/or 140 using one or moretraining or learning algorithms. One example training technique isbackwards propagation of errors. In some implementations, the modeltrainer 160 can perform supervised training techniques using a set oflabeled training data. In other implementations, the model trainer 160can perform unsupervised training techniques using a set of unlabeledtraining data. The model trainer 160 can perform a number ofgeneralization techniques to improve the generalization capability ofthe models being trained. Generalization techniques include weightdecays, dropouts, or other techniques.

In particular, the model trainer 160 can train a machine-learned model110 and/or 140 based on a set of training data 162. The training data162 can include, for example, training images labelled with a “correct”prediction. The model trainer 160 can be implemented in hardware,firmware, and/or software controlling one or more processors.

The computing system 102 can also include a network interface 124 usedto communicate with one or more systems or devices, including systems ordevices that are remotely located from the computing system 102. Thenetwork interface 124 can include any circuits, components, software,etc. for communicating with one or more networks (e.g., 180). In someimplementations, the network interface 124 can include, for example, oneor more of a communications controller, receiver, transceiver,transmitter, port, conductors, software and/or hardware forcommunicating data. Similarly, the machine learning computing system 130can include a network interface 164.

The network(s) 180 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) can include one or more of a local area network, wide areanetwork, the Internet, secure network, cellular network, mesh network,peer-to-peer communication link and/or some combination thereof and caninclude any number of wired or wireless links. Communication over thenetwork(s) 180 can be accomplished, for instance, via a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIG. 2 illustrates one example computing system 100 that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the computing system 102 caninclude the model trainer 160 and the training dataset 162. In suchimplementations, the machine-learned models 110 can be both trained andused locally at the computing system 102. As another example, in someimplementations, the computing system 102 is not connected to othercomputing systems.

In addition, components illustrated and/or discussed as being includedin one of the computing systems 102 or 130 can instead be included inanother of the computing systems 102 or 130. Such configurations can beimplemented without deviating from the scope of the present disclosure.The use of computer-based systems allows for a great variety of possibleconfigurations, combinations, and divisions of tasks and functionalitybetween and among components. Computer-implemented operations can beperformed on a single component or across multiple components.Computer-implemented tasks and/or operations can be performedsequentially or in parallel. Data and instructions can be stored in asingle memory device or across multiple memory devices.

Simplified Example Mask Generation

FIGS. 3A-C provide example illustrations of various stages of an exampleprocessing pipeline. FIG. 3A depicts a graphical diagram of exampleLIDAR imagery 300 according to example embodiments of the presentdisclosure. The imagery 300 is primarily sparse but does include anumber of pixels 302 a-d that have are non-sparse.

More particularly, in some instances, particularly those encountered byautonomous vehicle control systems, the input imagery is sparse innature. As one example, the input imagery can include LIDAR imageryproduced by a LIDAR system. For example, the LIDAR imagery can be athree-dimensional point cloud, where the point cloud is highly sparse.Stated differently, the point cloud can describe the locations ofdetected objects in three-dimensional space and, for many (most)locations in three-dimensional space, there was not an object detectedat such location. Additional examples of input imagery include imagerycaptured by one or more cameras or other sensors including, as examples,visible spectrum imagery (e.g., humanly-perceivable wavelengths);infrared imagery; imagery that depicts RADAR data produced by a RADARsystem; heat maps; data visualizations; or other forms of imagery.

In some implementations, the input imagery can be the three-dimensionalpoint cloud of LIDAR data but the imagery can be preprocessed prior togeneration of the binary mask. As one example, the three-dimensionalpoint can be preprocessed by projecting the three-dimensional pointcloud onto a two-dimensional view (e.g., a top-down or “bird's eye”view). Other preprocessing can optionally be performed such as, forexample, removing outliers, removing points that correspond to theground prior to projection, removing points associated with knownobjects already included in a map, or other preprocessing techniques. Abinary mask can then be generated with respect to the two-dimensionalview. For example, the two-dimensional view can be divided into pixelsand each pixel can be classified as sparse or non-sparse based on thenumber of data points included in such pixel. See, for example, imagery300 of FIG. 3A.

FIG. 3B depicts a graphical diagram of an example binary mask 350according to example embodiments of the present disclosure. The binarymask is primarily sparse, but does include a number of sections 352 a-cthat are non-sparse. Thus, for example, the pixels 302 a-c of imagery300 resulted in the corresponding sections 352 a-c of the mask 350 beingdesignated as non-sparse, while the pixel 302 d of the imagery did notresult in the corresponding section of the mask 350 being designated asnon-sparse. This may be, for example, that pixel 302 d was identified asan outlier or, as another example, may have failed to have enough dataassociated therewith to meet a threshold for designation as non-sparse.

More particularly, in some implementations, to extract the one or morerelevant portions of the imagery, the computing system can generate abinary mask. The binary mask can classify each of a plurality ofsections of the imagery as either sparse or non-sparse. For example, theplurality of sections of the imagery can correspond to pixels or voxelsof the imagery. Thus, in some examples, the binary mask can indicate,for each pixel or voxel included in the imagery, whether suchpixel/voxel is sparse or non-sparse. The computing system can determinethe one or more relevant portions of the imagery based at least in parton the binary mask.

As one example, the computing system can generate the binary mask bydividing the imagery into the plurality of sections (e.g., pixels orvoxels). The computing system can determine, for each of the pluralityof sections, an amount of data included in such section. The computingsystem can classify each section as either sparse or non-sparse based atleast in part on the amount of data included in such section. Forexample, in some implementations, if any amount of data is included inthe section (e.g., greater than zero), then the computing system canclassify such section as non-sparse, so that only sections that includeno data at all are classified as sparse. As another example, in someimplementations, the computing system can compare the amount of dataincluded in a section to a threshold amount of data (e.g., five datapoints) to determine whether such section is sparse or non-sparse.

To provide one example, in some implementations, the input imagery canbe a three-dimensional point cloud of LIDAR data. To generate the binarymask, the three-dimensional space can be divided into a plurality ofvoxels. The computing system can determine the amount of data (e.g., thenumber of LIDAR data points) included in each voxel and can classifyeach voxel as either sparse or non-sparse based on the amount of dataincluded in such voxel (e.g., as described above using a threshold ofzero or of some value greater than zero). Thus, in such example, thebinary mask can be a three-dimensional mask that classifies each voxelin three-dimensional space as sparse or non-sparse.

Alternatively or additionally to the mask generation techniquesdescribed above, in some implementations, the computing system caninclude or leverage a machine-learned mask generation model to generatethe binary mask. For example, the computing system can input the imageryinto the machine-learned mask generation model and, in response, receivethe binary mask as an output of the machine-learned mask generationmodel.

As one example, the machine-learned mask generation model can be aneural network, such as, for example, a convolutional neural network.For example, the machine-learned mask generation model can be viewed asan initial portion of a larger convolutional neural network thatprovides the prediction based on the relevant portions of the imagery.In some implementations, the machine-learned mask generation model canbe jointly trained with the convolutional neural network that providesthe prediction based on the relevant portions of the imagery in anend-to-end fashion (e.g., by backpropagating an error through all of thelayers sequentially).

In one example, the machine-learned mask generation model can be trainedor pre-trained based at least in part on training examples that includetraining imagery annotated with ground-truth labels of sparse sectionsand non-sparse sections (e.g., a training image and its corresponding“correct” mask). For example, the preprocessing techniques describedabove (e.g., projection plus amount of data analysis) can be used togenerate training data for the machine-learned mask generation model. Inanother example, the training examples can include segmentation masksused to pre-train the mask generation portion.

In some implementations, at training time, an approximation of a sparseconvolution can be performed by directly multiplying the results with adense binary mask. In some implementations, at training time, the maskis not constrained to be binary in nature, while the mask is constrainedor processed to be binary at inference time. This can avoid the problemof non-differentiable binary variables.

As another example technique to generate the binary mask, in someimplementations, the computing system can generate the binary mask byidentifying a region of interest within the imagery. The computingsystem can classify each section included in the region of interest asnon-sparse while classifying each section that is not included in theregion of interest as sparse.

As one example, the region of interest can be based at least in part oncontext data associated with an autonomous vehicle. For example, thecontext data associated with the autonomous vehicle can include aheading of the autonomous vehicle, a trajectory associated with theautonomous vehicle, and/or other state data associated with theautonomous vehicle or other objects in the surrounding environment.

Thus, to provide one example, portions of the imagery that depictregions of the surrounding environment that are “in front of” theautonomous vehicle and/or regions of the surrounding environment throughwhich the autonomous vehicle expects to travel can be considered to bethe region of interest. As such, portions of the imagery that correspondto regions of the surrounding environment that are behind the autonomousvehicle can be classified as sparse and not-convolved over, therebyimproving processing speed and efficiency. Further, this exampleimplementation can assist in reducing a delay or latency associated withcollection of LIDAR data, since the LIDAR system is not required toperform a complete 360 degree “sweep” but instead the LIDAR data can becollected and processed as soon as the LIDAR system has captured imagerythat corresponds to the region of interest.

As another example, the region of interest can be based at least in parton a confidence metric associated with one or more predictionspreviously obtained relative to a scene depicted by the imagery. Forexample, portions of the imagery for which the corresponding predictionshave low confidence can be included in the region of interest whileportions of the imagery for which the corresponding predictions havehigh confidence can be excluded from the region of interest. In suchfashion, portions of the imagery that have already been analyzed withhigh-confidence can be “ignored” since one would expect any furtherpredictions from the convolutional neural network to be redundant, whileportions of the imagery that have already been analyzed withlow-confidence can be included in the region of interest so that theconvolutional neural network provides an additional prediction withrespect to such portions as “a second opinion”.

To provide an example, an autonomous vehicle can include multiplesensors systems that have different modalities (e.g., cameras versusLIDAR system). First imagery captured by a first sensor (e.g., a camera)can be analyzed to receive a first set of predictions (e.g., predictionsthat detect objects in the surrounding environment as depicted by thecamera imagery). This first set of predictions can have a confidencemetric associated with each prediction. For example, a first detectedobject (e.g., bicyclist) can have a high confidence while a seconddetected object (e.g., pedestrian) or lack of detected object can have alow confidence assigned thereto. As such, a region of interest can bedefined for second imagery captured by a second sensor (e.g., LIDARsystem) based on the confidence metrics applied to the first imagery.For example, portions of the second imagery that may correspond to thefirst detected object may be excluded from the region of interest whileportions of the second imagery that correspond to the second detectedobject (or lack of detected object) may be included in the region ofinterest. As such, portions of the imagery that correspond to previouspredictions of high confidence can be classified as sparse andnot-convolved over, thereby improving processing speed and efficiency.

As another example, the region of interest can be based at least in parton an attention mechanism that tracks, in an iterative fashion, wherewithin the scene the attention of the processing system should befocused. For example, the locations at which portions of imagery weredetermined to be relevant in a past iteration can impact where thesystem searches for relevant imagery in a subsequent iteration (e.g., byguiding the region of interest based on the past imagery and/orpredictions derived from the past imagery).

In some implementations, the region of interest-based masking techniquescan be used in addition to the other masking techniques (e.g.,pixel-by-pixel data analysis) to generate a combined, final mask. Forexample, the final binary mask can be an intersection of the multipleinitial masks.

Once the computing system has generated the binary mask, the computingsystem can determine one or more relevant portions of the imagery basedat least in part on the binary mask.

As an example, FIG. 3C depicts a graphical diagram of an example image370 divided into example image portions according to example embodimentsof the present disclosure. The boundaries of the example image portionsare indicated by dashed lines. In the example illustrated in FIG. 3C,the portions 372 b and 372 c may be designated as relevant while theportion 372 a (and all other unnumbered portions) may be designated asnot relevant.

More particularly, as one example, in some implementations, thecomputing system can determine the one or more relevant portions of theimagery based at least in part on the binary mask by partitioning theimagery into a plurality of portions and classifying each portion asrelevant or not relevant.

For example, each portion can contain two or more of the plurality ofsections (e.g., pixel/voxel). The portions can be overlapping ornon-overlapping. The portions can be uniformly sized or non-uniformlysized. The portions can have a predefined size or can be dynamicallyfitted around the non-sparse sections. The size of the portions can bedifferent and individually optimized for different applications.

In one example, the portions can be predefined and uniformly sizedrectangles or boxes (e.g., depending on whether the imagery istwo-dimensional or three-dimensional), which can also be referred to as“tiles”. For example, each tile can cover a 9 pixel by 9 pixel area.

The computing system can classify each portion as either relevant or notrelevant based at least in part on the respective classifications of thesections contained in such portion as either sparse or non-sparse. Forexample, in some implementations, if any amount of the sections includedin the portion were classified as non-sparse, then the computing systemcan classify such portion as relevant, so that only portions thatinclude no sections that were classified as non-sparse are classified asnon-relevant. As another example, in some implementations, the computingsystem can compare the number of sections classified as non-sparseincluded in a portion to a threshold number of sections (e.g., threesections) to determine whether such portion is relevant or non-relevant.

In another example, the relevant portions can be fitted around thenon-sparse sections using a clustering/fitting algorithm. For example,the algorithm can seek to minimize both a total number of relevantportions and a total area covered by the relevant portions.

Thus, portions (e.g., tiles) of the input imagery can be designated asrelevant and can be extracted from the imagery for input into theconvolutional neural network. For example, extracting the relevantportions of the imagery can include cropping the imagery or otherwiseisolating the imagery data that corresponds to each relevant portion.

Example Sparse Blocks Network (SBNet)

The present disclosure demonstrates that block sparsity can be exploitedto significantly reduce the computational complexity of standardconvolutional and dense layers in deep neural networks. In particular,in many instances, the input data to a convolutional neural network hasblock-structured sparsity. For example, the neighborhood of azero-valued pixel is also likely to be zero. Therefore, in someimplementations, instead of skipping computation on at pixel level, anetwork can be configured to skip computation for an entire block ofactivations.

In some implementations, block sparsity can be defined in terms of amask that can be given by the problem definition or can be computed withlow cost operations. Example techniques for generating such a mask aredescribed throughout the present disclosure. As one example, in someimplementations, a road map can be exploited for LIDAR object detection,and a general model-predicted attention map can be exploited forcamera-based object detection. In some implementations, for speed-uppurposes, the computation mask can be fixed for every layer in thenetwork, while in other implementations it can be generalized to bedifferent per layer.

Generally, there are two major building blocks of the sparse block-wiseconvolution described herein:

Reduce mask to indices: A first stage can include converting a binarymask into sparse indices, where each index represents the location ofthe corresponding block in the input tensor. For example, each index canrepresent a rectangular block of size h×w. As one example, FIG. 5provides a graphical diagram of an example rectangular tiling forconverting a dense binary mask into sparse locations.

Sparse gathering/scattering operations: A second stage can includesparse gathering and scattering operations. In some implementations, forgathering, the computing system can extract a block from the inputtensor, given the start location (e.g., index) and the size of theblock. Scattering is the reverse operation where the input tensor isupdated with some data and their locations. As one example, FIG. 6provides a graphical diagram of an example sparse gathering/scatteringoperation, as performed by a proposed tiled sparse convolution module.

In the remainder of this section, the details of the above two buildingblocks are further described. Next, a residual unit for the sparse blockis discussed. The residual unit can group several layers of computationinto sparse blocks. Example implementations details are also provided.

Reduce Mask to Sparse Indices

Consider a feature map of size H×W×C. This is discussed with referenceto the case of 2D convolutions but it also applicable to arbitrarytensor inputs. Let M∈{0,1}^(H×W) be the binary mask representing thesparsity pattern. Aspects of the present disclosure take advantage ofnon-sparse convolution operations as they have been heavily optimized.Towards this goal, the sparse indices can be tiled with a set ofrectangles. Unfortunately, covering any binary shape with a minimalnumber of rectangles in an NP complete problem. Furthermore, havingrectangles that are of different shapes is not hardware friendly becauseof its difficulty to process different output dimensions in parallel.Therefore, some implementations of the present disclosure have a uniformblock size, so that the gathered blocks can be batched together torequire one single convolution operation.

In signal processing, “overlap-add” and “overlap-save” are twopartitioning schemes for performing convolutions with very long inputsignals. In some implementations, the sparse tiling algorithm can be aninstantiation of the “overlap-save” algorithm where overlapping blocksare gathered, but in the scattering stage, each thread writes tonon-overlapping blocks so that the writings are independent. Knowing theblock sizes and overlap sizes, a simple pooling operation can beperformed (e.g., max pooling or average pooling) to downsample the inputmask. The resulting non-zero locations are the block locations fromwhich patches are extracted. As one example, FIG. 5 provides a graphicaldiagram of an example rectangular tiling for converting a dense binarymask into sparse locations.

Sparse Gathering/Scattering

In some implementations, sparse gathering and scattering operations canconvert the network between dense and sparse modes. In someimplementations, unlike kernels that are implemented in deep learninglibraries (e.g., tf.gather_nd,tf.scatter_nd), the proposed kernel notonly operates on dense indices but also expands spatially to itsneighborhood window.

Example gather kernel: Given a list of non-sparse indices of size [B,3], which are the center locations of the blocks, and B is the number ofnon-sparse blocks, the blocks can then be sliced out of the 4-d[N×H×W×C] input tensor along height and width dimensions, and stackedalong the batch dimension to produce a tensor of [B×h×w×C].

Example scatter kernel: Scatter can perform the reverse operation ofgather, reusing the same input mask and block index list. The input toscatter kernel can be a tensor of shape [B×h′×w′×C]. The size of h′ andw′ can be computed based on a VALID (e.g., unpadded) convolution. If theinner convolution has kernel size [k_(h), k_(w)] and strides [s_(h),s_(w)], then

${h^{\prime} = \frac{h - k_{h} + 1}{s_{h}}},{{{and}\mspace{14mu} w^{\prime}} = {\frac{w - k_{w} + 1}{s_{w}}.}}$In the scatter kernel, the content of the convolution results can becopied back to the full activation tensor.

In some implementations, the overhead of gather/scatter operations canbe amortized across entire columns of a convolutional neural network. Asone example, in some instances, the column can be a ResNet block, butthe columns can be larger as well.

FIG. 7 provides a graphical diagram of a simplified example of input andoutput tensors according to example embodiments of the presentdisclosure. In particular, the simplified example of FIG. 7 has a blocksize of 5, a kernel size of 3×3, and kernel strides of 2×2. Blockstrides can be computed as k-s=3−2=1.

Sparse Residual Units

The sparse block convolutions proposed herein also integrate well withresidual units. In some implementations, a single residual unit containsthree convolution, batch norm, and ReLU layers, all of which can beoperated under sparse mode. The total increase in receptive field of aresidual unit can be the same as a single 3×3 convolution. Therefore, insome implementations, all nine layers can share a single gathering andscattering operation without growing the overlap area between blocks. Inaddition to the computation savings, batch-normalizing across non-sparseelements contributes to better performance since it ignores non-validdata that may introduce noises to the statistics. As one example, FIG. 8provides graphical diagrams of an example regular residual unit and anexample sparse residual unit according to example embodiments of thepresent disclosure. In particular, FIG. 8 shows a computation graph ofthe sparse residual unit. In some implementations, a neural network caninclude a plurality of sparse residual units (e.g., stacked one afterthe other).

In some implementations, end-to-end training of sparse networks isrequired since the batch normalization statistics may be differentbetween full scale activations and dense-only activations. In someimplementations, the gradient of a scatter operation is simply thegather operation with the same precomputed block indices executed on thenext layer's backpropagated gradient tensor and vice versa sincegather/scatter act as a mask while backpropagating the gradient. Whencalculating the gradients of an overlapping gather operation, thescatter may perform atomic addition of gradients on the edges ofoverlapping tiles.

Example Implementation Details

One aspect of the present disclosure is an implementation of a blockconvolution algorithm using customized CUDA kernels. As has been shownexperimentally, this results in significant speed up in terms ofwall-clock time. Example implementation details are provided below.

Fused downsample and indexing kernel: To minimize the intermediateoutputs between kernels, the downsample and indexing kernels can befused into one. Inside each tile, a fused max or average poolingoperation can be computed followed by writing out the block index into asequential index array using GPU atomics to increment the block counter.Thus the input is a [N×H×W] tensor and the output is a list of [B, 3]sparse indices referring to full channel slices within each block.

Fused transpose and gathering/scattering kernel: When doing 2D spatialgather and scatter, NHWC format can be preferred because of the spatialaffinity: in NHWC format, every memory block of size w×C is contiguous,whereas in NCHW format, only every block of size w is contiguous.Because of cuDNN's native performance favoring NCHW convolutions andbatch normalizations, an example gather/scatter kernel of the presentdisclosure also fuses the transpose from NHWC to NCHW tensor formatinside the same kernel. This also saves a memory round-trip for doingadditional transpose operations. Under this implementation, the gatherkernel outputs tensor of shape [B, C, h, w], and the scatter kerneltakes tensor of shape [B, C, h′, w′].

Fused scatter-add kernel for residual blocks: For ResNet architectureduring inference, the input tensor can be reused for output so that anextra memory allocation is avoided and there is no need to wipe theoutput tensor to be all zeros. In some implementations, a fused kernelof 2D scatter and addition can be used, where only the non-sparselocations are updated by adding the convolution results back to theinput tensor. If the convolution layer has stride larger than 1, one canuse the output tensor in the shortcut connection in ResNet architectureas the base tensor, and update non-sparse results on top.

Example Methods

FIG. 4 depicts a flow chart diagram of an example method 400 to apply aconvolutional neural network to sparse imagery according to exampleembodiments of the present disclosure.

At 402, a computing system can obtain imagery. As examples, the imagerycan include LIDAR data and/or visible spectrum imagery. The imagery canbe two-dimensional or three-dimensional. In some implementations, thecomputing system can preprocess three-dimensional imagery by projectingthree-dimensional imagery onto a two-dimensional view.

At 404, the computing system can extract one or more relevant portionsof the imagery.

In some implementations, extracting the one or more relevant portions ofthe imagery at 404 can include identifying one or more non-sparseregions of the imagery and extracting the one or more relevant portionsthat respectively correspond to the one or more non-sparse regions.

In some implementations, extracting the one or more relevant portions ofthe imagery at 404 can include generating a binary mask that classifieseach of a plurality of sections of the imagery as either sparse ornon-sparse and determining the one or more relevant portions of theimagery based at least in part on the binary mask.

As one example, generating the binary mask can include dividing theimagery into the plurality of sections; determining, for each of theplurality of sections, an amount of data included in such section; andclassifying each section as either sparse or non-sparse based at leastin part on the amount of data included in such section.

As another example, generating the binary mask can include inputting theimagery into a machine-learned mask generation model and receiving thebinary mask as an output of the machine-learned mask generation model.

As yet another example, generating the binary mask can includeidentifying a region of interest within the imagery; classifying eachsection included in the region of interest as non-sparse; andclassifying each section that is not included in the region of interestas sparse.

As one example, identifying the region of interest can includeidentifying the region of interest based at least in part on contextdata associated with an autonomous vehicle. For example, the contextdata can include a heading of the autonomous vehicle.

As another example, identifying the region of interest can includeidentifying the region of interest based at least in part on aconfidence metric associated with one or more predictions previouslyobtained relative to a scene depicted by the imagery.

In some implementations, determining the one or more relevant portionsof the imagery based at least in part on the binary mask can includepartitioning the imagery into a plurality of portions. Each portion cancontain two or more of the plurality of sections. Determining the one ormore relevant portions can further include classifying each portion aseither relevant or not relevant based at least in part on the respectiveclassifications of the sections contained in such portion as eithersparse or non-sparse.

At 406, the computing system can provide each of the one or morerelevant portions of the imagery to a convolutional neural network. Insome implementations, providing each of the one or more relevantportions of the imagery to the convolutional neural network at 406 caninclude stacking the one or more relevant portions in a depth-wisefashion to form a tensor and inputting the tensor into the convolutionalneural network

In some implementations, a kernel of the machine-learned convolutionalneural network has a kernel size that is larger than at least one of oneor more portion sizes respectively associated with at least one relevantportion of the one or more relevant portions of the imagery. In suchimplementations, providing each of the one or more relevant portions ofthe imagery to the convolutional neural network at 406 can includeidentifying at least one kernel portion that respectively corresponds tothe at least one relevant portion for which the kernel size is largerthan the corresponding portion size and computing a layer of themachine-learned convolutional neural network with respect to the atleast relevant portion by computing only the identified kernel portionagainst the at least one relevant portion.

In some implementations, providing each of the one or more relevantportions of the imagery to the convolutional neural network at 406 caninclude inputting each of the one or more relevant portions intorespective parallel instances of the machine-learned convolutionalneural network in parallel.

At 408, the computing system can receive at least one prediction as anoutput of the convolutional neural network. In some implementations,receiving the prediction from the machine-learned convolutional neuralnetwork at 408 can include patching one or more prediction results tothe imagery. For example, the one or more prediction results that arepatched can respectively correspond to the one or more relevantportions.

In some implementations, the imagery can be imagery captured by one ormore sensors of an autonomous vehicle and the prediction from themachine-learned convolutional neural network can be a detection of anobject in a surrounding environment of the autonomous vehicle.

In some implementations, the imagery can be imagery captured by one ormore sensors of an autonomous vehicle and the prediction from themachine-learned convolutional neural network can be a predictedtrajectory for an object in a surrounding environment of the autonomousvehicle.

In some implementations, method 400 can further include determining amotion plan for the autonomous vehicle based at least in part on the atleast one prediction output by the convolutional neural network andcontrolling motion of the autonomous vehicle based at least in part onthe motion plan.

Additional Disclosure

The technology discussed herein makes reference to servers, databases,software applications, and other computer-based systems, as well asactions taken and information sent to and from such systems. Theinherent flexibility of computer-based systems allows for a greatvariety of possible configurations, combinations, and divisions of tasksand functionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

In particular, although FIG. 4 depicts steps performed in a particularorder for purposes of illustration and discussion, the methods of thepresent disclosure are not limited to the particularly illustrated orderor arrangement. The various steps of the method 400 can be omitted,rearranged, combined, and/or adapted in various ways without deviatingfrom the scope of the present disclosure.

What is claimed is:
 1. A computing system for processing imagerycaptured by one or more sensors of an autonomous vehicle, comprising:one or more processors; a machine-learned convolutional neural network;and one or more non-transitory computer-readable media that storeinstructions that, when executed by the one or more processors, causethe computing system to perform operations, the operations comprising:obtaining the imagery captured by the one or more sensors of theautonomous vehicle; extracting one or more relevant portions of theimagery, the one or more relevant portions being less than an entiretyof the imagery, wherein extracting the one or more relevant portions ofthe imagery comprises: generating a binary mask that classifies each ofa plurality of sections of the imagery as either sparse or non-sparse;and determining the one or more relevant portions of the imagery basedat least in part on the binary mask; providing each of the one or morerelevant portions of the imagery to the machine-learned convolutionalneural network, wherein the machine-learned convolutional neural networkperforms one or more convolutions respectively on each of the one ormore relevant portions; and receiving a prediction from themachine-learned convolutional neural network based at least in part onthe one or more convolutions respectively performed on each of the oneor more relevant portions.
 2. The computing system of claim 1, wherein:the computing system is on-board the autonomous vehicle; the imagerycomprises one or both of: LIDAR data captured by a LIDAR system mountedon the autonomous vehicle; and one or more image frames captured by oneor more cameras mounted on the autonomous vehicle; the prediction fromthe machine-learned convolutional neural network comprises one or bothof: detection of an object in a surrounding environment of theautonomous vehicle; and a predicted trajectory for the object in thesurrounding environment of the autonomous vehicle.
 3. The computingsystem of claim 1, wherein the operations further comprise controllingmotion of the autonomous vehicle based at least in part on theprediction received from the machine-learned convolutional neuralnetwork.
 4. The computing system of claim 1, wherein extracting the oneor more relevant portions of the imagery comprises: identifying one ormore non-sparse regions of the imagery; and extracting the one or morerelevant portions that respectively correspond to the one or morenon-sparse regions.
 5. The computing system of claim 1, whereingenerating the binary mask comprises: dividing the imagery into theplurality of sections; determining, for each of the plurality ofsections, an amount of data included in such section; and classifyingeach section as either sparse or non-sparse based at least in part onthe amount of data included in such section.
 6. The computing system ofclaim 1, wherein generating the binary mask comprises: inputting theimagery into a machine-learned mask generation model; and receiving thebinary mask as an output of the machine-learned mask generation model.7. The computing system of claim 1, wherein generating the binary maskcomprises: identifying a region of interest within the imagery;classifying each section included in the region of interest asnon-sparse; and classifying each section that is not included in theregion of interest as sparse.
 8. The computing system of claim 7,wherein identifying the region of interest comprises identifying theregion of interest based at least in part on context data associatedwith an autonomous vehicle.
 9. The computing system of claim 8, whereinthe context data associated with the autonomous vehicle comprises aheading of the autonomous vehicle.
 10. The computing system of claim 7,wherein identifying the region of interest comprises identifying theregion of interest based at least in part on a confidence metricassociated with one or more predictions previously obtained relative toa scene depicted by the imagery.
 11. The computing system of claim 1,wherein determining the one or more relevant portions of the imagerybased at least in part on the binary mask comprises: partitioning theimagery into a plurality of portions, each portion containing two ormore of the plurality of sections; and classifying each portion aseither relevant or not relevant based at least in part on the respectiveclassifications of the sections contained in such portion as eithersparse or non-sparse.
 12. The computing system of claim 1, wherein:providing each of the one or more relevant portions of the imagery tothe machine-learned convolutional neural network comprises: stacking theone or more relevant portions in a depth-wise fashion to form a tensor;and inputting the tensor into the convolutional neural network; andreceiving the prediction from the machine-learned convolutional neuralnetwork comprises patching one or more prediction results to theimagery, wherein the one or more prediction results respectivelycorrespond to the one or more relevant portions.
 13. The computingsystem of claim 1, wherein: a kernel of the machine-learnedconvolutional neural network has a kernel size that is larger than atleast one of one or more portion sizes respectively associated with atleast one relevant portion of the one or more relevant portions of theimagery; and providing each of the one or more relevant portions of theimagery to the machine-learned convolutional neural network comprises:identifying at least one kernel portion that respectively corresponds tothe at least one relevant portion for which the kernel size is largerthan the corresponding portion size; and computing a layer of themachine-learned convolutional neural network with respect to the atleast relevant portion by computing only the identified kernel portionagainst the at least one relevant portion.
 14. The computing system ofclaim 13, wherein the machine-learned convolutional neural networkperforms only valid convolutions in which a kernel size is smaller thana corresponding portion size.
 15. The computing system of claim 1,wherein the machine-learned convolutional neural network includes aresidual block and the residual block is configured to provide a sparseupdate to hidden features.
 16. An autonomous vehicle, comprising: one ormore sensors that capture imagery; one or more processors; amachine-learned convolutional neural network; and one or morenon-transitory computer-readable media that store instructions that,when executed by the one or more processors, cause the autonomousvehicle to perform operations, the operations comprising: obtaining theimagery captured by the one or more sensors of the autonomous vehicle;extracting one or more relevant portions of the imagery, the one or morerelevant portions being less than an entirety of the imagery, whereinextracting the one or more relevant portions of the imagery comprises:generating a binary mask that classifies each of a plurality of sectionsof the imagery as either sparse or non-sparse; and determining the oneor more relevant portions of the imagery based at least in part on thebinary mask; providing each of the one or more relevant portions of theimagery to the machine-learned convolutional neural network, wherein themachine-learned convolutional neural network performs one or moreconvolutions respectively on each of the one or more relevant portions;and receiving a prediction from the machine-learned convolutional neuralnetwork based at least in part on the one or more convolutionsrespectively performed on each of the one or more relevant portions.